Conjoint Analysis And Realism In Price Research

Not long ago I received a survey from a trade association testing the appeal of market research online training courses using concept testing. They also wanted to know, how much I would pay for these courses using the Van Westendorp Price Sensitivity Meter (PSM ).

In my opinion, the best approach they should have adopted in this case is Conjoint Analysis, which provides useful insights for product development, competitive positioning, brand equity measurement, market segmentation, and especially price research.

Conjoint analysis includes a set of trade-off analysis methods such as Full Profile Conjoint Analysis, Adaptive Conjoint Analysis, Choice-Based Conjoint Analysis (CBC), and Menu-Based Conjoint Analysis (MBCA). Which method should we use? The one that better reflects how buyers make decisions in the marketplace.

Choice-Based Conjoint Analysis (CBC) is widely used since it tries to mimic the actual purchase decision process for products within a competitive context. In CBC, we present different product configurations to respondents and ask which they would choose or purchase. Products are configured using a set of relevant attributes, including price.

As an example, to conduct a Choice-Based Conjoint Analysis for the aforementioned online market research training classes, we would follow the following steps.

• Identify key attributes (or factors) and levels. This is the most important step in the design of conjoint analysis studies, as the selection of the proper attributes will have an impact on our ability to represent accurately how buyers make purchase decisions. Qualitative research or other research sources should be used to provide realistic attributes. The presentation of attribute levels is not restricted to text. We can use images, when appropriate.  Attributes or factors should: 
  • Cover the full range of possibilities for existing products
  • Be independent from each other, with no overlapping meaning
  • Be mutually exclusive
  • Have a balanced number of levels across attributes (when possible)

  The table below has an example of attributes that could have been included to test online market research courses. 

   

  

   

  • Create an experimental design. This should provide frequency balance (each attribute appears the same number of times), orthogonality (each item is paired with other items the same number of times), and position balance (each items appears the same number of times in each position). The experimental design is then used to generate a certain number of choice tasks for respondents. Below is an example of a potential choice task for online market research training courses.

  

   

   Using this approach for the case of online market research courses, would not only provide a more realistic scenario for respondents but also prevent them from focusing solely on price, decreasing the natural tendency to low-ball, when price becomes the center of attention, as it happens in pricing research approaches using direct questions about willingness to pay, purchase intent or the Van Westendorp PSM.

   

  •  Estimate utility coefficients. These are measures of desirability for each of the attribute levels and can be estimated with different methods including aggregated multinomial logit, latent class analysis, or Hierarchical Bayes estimation (more commonly used these days).
  • Develop a market simulator. Simulators use the estimated utility coefficients to predict which product configurations are more likely to be chosen, among many product configurations, including those that were not presented to respondents. This is the most valuable tool for managers, as they can conduct “what-if” analyses to predict shares of preferences for different product configurations.  

  In the case of the online market research course, the market simulator example below,  Option 1 could get the highest share of preference likely due, not only to a lower price point but also to the availability of a Q&A option. Option 3, which doesn’t have a Q&A option, with a similar price point to Option 1, could get the lowest share of preference. Even Option 2, which offers Q&A with a live instructor, could receive a higher share of preference, despite having the highest price.

   

  

   

  Simpler price research approaches are appealing because of their simplicity, affordability, and ease of implementation, which suit many managers who don’t have much experience with statistics or feel uncomfortable with advanced techniques such as Conjoint Analysis. Unfortunately, simpler approaches often don’t reflect how people make buying decisions and can result in misleading conclusions.

  As for cost, the field of conjoint analysis is constantly evolving. New techniques have become more accessible and affordable to research practitioners as computational tools have become commercially available. A conjoint analysis study is likely to be more expensive than a traditional concept test, but they are worth it as it allows us to get closer to more realistic decision-making situations price research.